KATP channel dependent heart multiome atlas

Plasmalemmal ATP sensitive potassium (KATP) channels are recognized metabolic sensors, yet their cellular reach is less well understood. Here, transgenic Kir6.2 null hearts devoid of the KATP channel pore underwent multiomics surveillance and systems interrogation versus wildtype counterparts. Despite maintained organ performance, the knockout proteome deviated beyond a discrete loss of constitutive KATP channel subunits. Multidimensional nano-flow liquid chromatography tandem mass spectrometry resolved 111 differentially expressed proteins and their expanded network neighborhood, dominated by metabolic process engagement. Independent multimodal chemometric gas and liquid chromatography mass spectrometry unveiled differential expression of over one quarter of measured metabolites discriminating the Kir6.2 deficient heart metabolome. Supervised class analogy ranking and unsupervised enrichment analysis prioritized nicotinamide adenine dinucleotide (NAD+), affirmed by extensive overrepresentation of NAD+ associated circuitry. The remodeled metabolome and proteome revealed functional convergence and an integrated signature of disease susceptibility. Deciphered cardiac patterns were traceable in the corresponding plasma metabolome, with tissue concordant plasma changes offering surrogate metabolite markers of myocardial latent vulnerability. Thus, Kir6.2 deficit precipitates multiome reorganization, mapping a comprehensive atlas of the KATP channel dependent landscape.

www.nature.com/scientificreports/ lated metabolites contributed to 4 and upregulated metabolites to all 6 pathway macroclusters (Fig. 4D). Kir6.2 deletion precipitated a distinct pattern of change. The percent of metabolites changed in each pathway macrocluster ranged from 17 to 35% (Fig. 4D, upper inset). Specifically, the number of metabolites significantly changed were: 16 (12 up, 4 down) out of 53 in the amino acid cluster; 6 (up) out of 27 in the carbohydrate cluster; 2 (1 up, 1 down) out of 6 in the cofactor/vitamin cluster; 1 (up) out of 6 in the energy cluster; 26 (18 up, 8 down) out of 100 in the lipid cluster; and 8 (5 up, 3 down) out of 23 in the nucleotide cluster. Notably, 100% predictive classification accuracy across cohorts was achieved in Random Forest modeling using the top 30 differential metabolites (Fig. 4D, lower inset). Thus, the resolved chemometric fingerprint mapping the extent and diversity of metabolite changes readily distinguished KO from WT hearts, underscoring the impact of K ATP channel deficiency on the cardiac metabolome.
Kir6.2 dependent metabolic prioritization. Supervised classification of the metabolome by soft independent modeling of class analogy (SIMCA) validated KO and WT intra-group consistency and inter-cohort separation, as evident by partial least squares-discriminant analysis (PLS-DA; Fig. 5A). Systems modeling by SIMCA identified 28 metabolites with variable importance in projection (VIP) scores exceeding 1.5, affirming their prominence in group segregation (Fig. 5B). The top scoring metabolite was nicotinamide adenine dinucleotide (NAD + ; reduced in KO by ≈ 30% from WT levels). In parallel, nicotinate and nicotinamide metabolism was the top pathway for cohort discrimination. The Kir6.2 dependent differential metabolome was expanded to a 135 node scale-free interactome (Fig. 5C). Unsupervised classification by Metabolite Pathway Analysis (MetPA) of the interactome corroborated the preeminence of NAD + and the nicotinate and nicotinamide pathway (Fig. 5D), with 75% of the most significant MetPA pathways confirmed among the top pathways modeled by VIP scoring (Fig. 5D, bold italicized font). While NAD + levels were significantly reduced in response to Kir6.2 ablation (P = 1.37 × 10 −7 ; Fig. 5E, left), flavin adenine dinucleotide (the other primary electron acceptor) did not differ between WT and KO cohorts (P = 0.55; Fig. 5E, right). Consistent with NAD + prioritization by unsupervised and supervised systems interrogation, NAD + was associated with the greatest number of metabolic and signaling pathways enriched in KO hearts (Fig. 6A,B). Notably, 61% (22/36) of enriched Ingenuity Pathway Analysis (IPA) canonical pathways were NAD + related (Fig. 6A). Less preeminent was glycine linked to 12 enriched pathways, followed by l-glutamine (7 pathways), xanthine (6), l-tyrosine (5), and 4 or fewer IPA enriched pathways for the remaining 22 metabolites. Likewise, 95% (60/63) of enriched Metabolite Set Enrichment Analysis (MSEA) pathways were associated with NAD + (Fig. 6B). In contrast, second-ranked glycine was associated with only 9 of the 63 pathways. Additional metabolites linking to MSEA enriched pathways included l-glutamine (7 pathways), glycerol-3-phosphate (6), and β-alanine (4), with 3 or fewer enriched pathways linking to each of the remaining 21 differential metabolites. Concordant with an NAD + -centric KO metabotype, the corresponding Kir6.2 dependent proteome displayed altered expression of 9 proteins associated with NAD + biosynthesis, consumption, or utilization (Fig. 6C). Complementary interrogation thus identified altered metabolites prioritizing key pathways delineating the metabolic identity of the Kir6.2 deficient state.
Distinct Kir6.2 knockout plasma reflects heart metabolome. Functional enrichment analysis of the resolved differential plasma metabolome recapitulated 94% of the 36 functional traits enriched in the corresponding heart metabolome (Supplementary Table 5). Over one quarter of Kir6.2 dependent tissue metabolome changes (16/59) were also detected as differentially expressed in plasma (Fig. 9A, upper). Of these common changes, 94% (15/16) exhibited concordant direction of change in response to Kir6.2 deletion, with 10 upregulated and 5 downregulated metabolites spanning metabolic pathways (Fig. 9A, lower). This shared core included the metabolites prioritized by both SIMCA VIP scoring and Random Forest modeling, namely p-cresol sulfate and N-acetylornithine (see also Fig. 8A,C), offering a plasma readout of tissue level change (Fig. 9B). The differential plasma metabolome reproduced the disease and disorder enrichment associations prioritized in the corresponding heart tissue (Fig. 9C). Matching the extent of heart damage susceptibility predicted from the tissue metabolome, the plasma metabolome prognosticated cardiovascular adverse outcome (Fig. 9D). Tissue concordant differential metabolites within the plasma metabolome thus represent potential reporter molecules of latent cardiac susceptibility associated with Kir6.2 deficiency.

Discussion
The present study demonstrates that hearts deprived of the Kir6.2 K ATP channel pore undergo a proteomic and metabolomic overhaul beyond constitutive channel subunits. The distinct proteome and metabolome conversion underpinned adaptation in hearts lacking functional K ATP channels. Deep phenotyping characterized a metabo-centric metamorphosis across the molecular infrastructure and biochemical output of Kir6.2 devoid hearts, compromised by an imprint of disease susceptibility. The resolved Kir6.2 dependent interactome highlights the centrality of intact K ATP channels in proteome and metabolome maintenance ensuring heart resilience. A systems biology strategy was here employed to acquire and interpret molecular information sampled in vivo across complementary proteomic and metabolomic dimensions 39 (Fig. 10). Proteomic surveillance of the myocardium identified over 56,000 peptides representing 4846 proteins, enabling untargeted capture of the Kir6.2 dependent expression change spectrum. The high stringency design pinpointed 111 altered proteins across a range of vital cellular processes, demonstrating metabolic primacy of the remodeled K ATP channel deficient heart proteome. Comprehensive protein cataloging extended the findings of more targeted approaches linking metabolism with the cardiac K ATP channel at local partner, associated pathway, or subproteome levels [40][41][42][43][44][45] . Specificity of observed changes attributed to plasmalemmal K ATP channel integrity was supported by unaltered expression of Mitok and Mitosur, in line with a distinct, non-redundant, channel identity in subcellular compartments 46 .
Underpinnings of metabolic prioritization were further mined by unbiased evaluation of the cardiac K ATP channel dependent metabolome. Multidimensional chemometric profiling revealed that 27% of ventricular metabolites were altered in response to Kir6.2 ablation, spanning metabolic families. The metabolomic changes provoked by Kir6.2 ablation are comparable in magnitude to those characterizing hearts with compromised energy regulators or failing hearts 47,48 .
Notably, Kir6.2 dependent metabolome and proteome enriched functions exhibited remarkable overlap (97% for the metabolome and 95% for the proteome), revealing convergence across platform readouts. Screening multiple omics layers from the same source, in conjunction with data inclusivity free of selection and interpretation bias, supports the validity and utility of considering unique yet interrelated datasets 49,50 . Taken together, the congruent interrogation over multiple molecular strata underscored the impact of K ATP channels as an influential nexus in cardiac metabolism. The 111 proteins significantly altered (FDR corrected P < 0.05) in Kir6.2 knockout (KO, n = 10) relative to wildtype (WT, n = 10) heart extracts, including 68 upregulated (upper) and 43 downregulated (lower), are listed by gene symbol with log 2 fold change (FC) values, and clustered into primary biological process categories from greatest to least extensive change. Proteins denoted 'KO > > ' or 'WT > > ' were detected in 50% or more of the specified cohort and undetected in the other group. Proteome impact was most prominent for 'Metabolism, Catabolism' (n = 28 proteins, 16 up and 12 down), followed by: 'Signaling, Transport, and Motility' (n = 26, 12 up, 14 down); 'Immunity, Inflammation' (n = 14 up); 'Morphology, Structure' (n = 10, 9 up, 1 down); 'Stress, Stimulus Response' (n = 8, 3 up, 5 down); 'Protein PTMs, Processing' (n = 6, 4 up, 2 down); 'Transcription, Epigenetics, DNA' (n = 6, 3 up, 3 down); 'Differentiation, Development' (n = 5, 1 up, 4 down); 'Biosynthesis' (n = 4, 2 up, 2 down); ' Apoptosis, Cell Death' (n = 1 up); 'Cell Cycle' (n = 1 up); and 2 proteins that remain 'Uncharacterized' . PTMs = post-translational modifications. (B) Bubble plot of the Kir6.2 dependent differential proteome derived network (see Supplementary Figure) prioritized metabolism among enriched biological processes (P < 0.001). Enriched biological processes were grouped into distinct clusters (see also Supplementary Table 2). Circle diameters are proportional to the number of enriched biological process annotations per cluster and centered at the harmonic mean P-value (−log) for cluster constituents. Calculated as the reciprocal of the arithmetic mean of the reciprocal for all P-values in a cluster, the harmonic mean applies Bayesian modeling to account for mutually exclusive P-values that are not independent of one another.    Agglomerative clustering by correlation distance and average linkage of z-score transformed differential metabolites, with differential cohort upregulation (purple) and downregulation (green) in response to Kir6.2 deletion, was distributed across multiple metabolic pathways. Identified metabolites spanned 7 pathway macroclusters (D upper inset, with number of detected metabolites in each pathway indicated) with differential expression distributed across 6 of the 7, impacting 17-35% of detected metabolites per pathway macrocluster. Random Forest modeling of the top thirty differential metabolites accurately classified WT from KO (D lower inset). www.nature.com/scientificreports/ Across the breadth of K ATP channel dependent reorganization, systems deconvolution prioritized the multivalent coenzyme NAD + and its associated metabolic pathways. The decrease in NAD + in Kir6.2 deficient hearts was paralleled by change in NAD + associated proteins, including upregulation of NAD + salvage enzymes, namely the metazoan spot homologue 1 (Hddc3) 51 and renalase (Rnls) 52 . Maintenance of NAD + is vital to tissue homeostasis 53,54 , with myocardial NAD + pool derangement associated with metabolic remodeling in heart failure and supplementation preserving cardiac performance [55][56][57] . Notably, NAD + at physiological concentrations regulates K ATP channel activity 58 , and a nicotinamide-rich diet upregulates K ATP channel expression and increases myocardial resilience 59 . In this context, the present findings support a reciprocal relationship of K ATP channels and metabolism, and reveal that the Kir6.2 null heart is typified by NAD + deficit, a prominent feature of cardiomyopathy prone environments 60 .

Scientific Reports
Indeed, dual metabolome and proteome assessment of the Kir6.2 knockout heart exposed an acquired predisposition to disease susceptibility. This vulnerability signature was herein evident in the young adult at an age apparently free from Kir6.2 dependent extracardiac confounders such as altered insulin secretion, glucose tolerance, and muscle properties 61 . The molecular imprint of heart disease susceptibility was present in advance of overt physiological dysfunction, suggesting that molecular reorganization in response to Kir6.2 deletion is a compensatory adaptation in the young adult animal. Documented independently or collectively across profiling modalities, the current multiomics findings build on initial single omic exploration of Kir6.2 loss 62 . The predictive imprint of disease risk is further reinforced by overt organ failure compromising K ATP channel deficient hearts subjected to stress [63][64][65][66][67][68][69] . K ATP channels are implicated in the maintenance of cellular homeostasis, recognized as early responders to metabolic challenge 70 . The mechanism by which Kir6.2 ablation mediates subcellular adaptation needs further study. In principle the observed proteome and metabolome remodelling could be related to the energetically costly KO heart's propensity for exaggerated Ca 2+ loading 9,11,12,22 . Calcium overload has been directly implicated in cellular transformation at the protein and metabolite level 71 . Here none of the identified proteins involved in Ca 2+ handling, regulation, or homeostasis differed in expression between WT and KO (see Supplemental Table 1). This would suggest that omic alterations could be mediated by a proclivity for Ca 2+ loading on a beat-to-beat basis, rather than a structural change across the Ca 2+ regulatory proteome.
Corroborating the cardiac disease risk exposed at the tissue level, the resolved K ATP channel dependent plasma metabolome independently reflected myocardial susceptibility. Diverse pathological processes associated with organ failure can be monitored by blood biomarkers, serving as molecular surrogates for early disease diagnosis, stratification, and detection at an asymptomatic state 72 . Among concordant differential metabolites shared between tissue and plasma, p-cresol sulfate and N-acetylornithine were consistently prioritized across applied modeling algorithms. Upregulation of p-cresol sulfate and downregulation of N-acetylornithine have been associated with cardiovascular disease, namely in (a)symptomatic cardiac dysfunction and incident heart failure [73][74][75][76] . These candidate biomarkers offer a clinically applicable and readily accessible source for detecting K ATP channel dependent vulnerability.
Limitations in proteomic and metabolomic analyses may arise from small sample number, restricted data inclusivity, absence of cross-validation, or inadequate application of interrogation resources [77][78][79][80] . Here, quality control ensured that the extended cohort size used was adequately powered to capture distinct patterns at high resolution. Moreover, high throughput screening was applied without imposed constraints for inclusive data input, avoiding inadvertent biases. Examining datasets with, and extracting common signatures from, multiple algorithms here provided added confidence in interpretation. Accordingly, supervised and unsupervised approaches were systematically employed following best practices, generating matching output across platforms. Additionally, examination of the heart and plasma in a global deletion model must account for potential confounding effects arising from extracardiac influences. To mitigate this possibility in the present study where Kir6.2 expression in pancreas and skeletal muscle was also impacted, young adult mice (< 4 months of age) were chosen for analysis at an age when insulin secretion, glucose tolerance, and skeletal muscle properties are known to be equivalent between WT animals and those with Kir6.2 deletion 61 .
In conclusion, an atlas of K ATP channel dependent interactome was here constructed using an unbiased systems strategy integrating proteome and metabolome strata. Multiomics surveillance of Kir6.2 null hearts mapped a metabo-centric landscape, exposing latent vulnerability further traceable in the plasma metabolome. The captured multidimensionality of the K ATP channel reliant bioenergetic system offers a broadened perspective on a vital contributor to cardiac homeostasis.

Methods
Ethics approval. Protocols were approved by the Mayo Clinic Institutional Animal Care and Use Committee, following National Institutes of Health guidelines. Reporting of animal studies here follows the recommendations in the ARRIVE guidelines 81 . Mice were young adult (up to 4 month-old) male WT (C57BL6) and age-, sex-, environment-matched Kir6.2 null K ATP channel KO counterparts. Of note, up to this age, KO mice maintain insulin secretion, glucose tolerance, and skeletal muscle properties within a normal range 61 . In vivo physiology. Group Nano-flow liquid chromatography tandem mass spectrometry. Gel tranches were de-stained, with protein reduced, alkylated, digested with trypsin, and peptides extracted and dried 89      www.nature.com/scientificreports/ Mass spectrometry data analysis. Raw files consisting of 10 LC-MS/MS runs per sample were processed in MaxQuant 1.6.7.0 90 , using Andromeda search engine for label-free quantification (LFQ), with applied fastLFQ settings. Spectra were searched against UniProt mouse entries, combining forward and reverse peptides as decoys to estimate FDR, with peptide match and protein assignment FDR set at 0.01. Search parameters included trypsin/P digestion, cysteine carbamido-methylation, and variable modifications of amino-terminal protein acetylation, glutamate to pyro-glutamate, and methionine oxidation. Maximum charge was + 7, with up to 3 dynamic modifications, maximum of 2 missed cleavages, and minimum of 7 amino acids. Mass tolerance was 20 and 10 ppm for first and main searches. LFQ identification was maximized by MaxQuant's 'Match Between Runs' feature, assigning identified spectra from one LC-MS/MS run to corresponding aligned mass and retention time spectra in other runs. Peptides were rolled into protein assignments, requiring ≥ 2 peptides per protein.
Differential expression. Relative protein abundance was calculated in R (cran.r-project.org) using Proteus 91 , for limma analysis 92 of label-free MaxQuant data. Peptide information acquired from MaxQuant evidence files was filtered for contaminants and reverse peptides without imputing missing values. Peptides were rolled into corresponding proteins, data median normalized, and the high-flyer method applied to calculate relative protein abundance. Proteins with FDR corrected P < 0.05 were considered differentially expressed. Gas chromatography mass spectrometry. For GC/MS of volatile metabolites, samples were re-dried under vacuum prior to derivatization under N 2 using bistrimethyl-silyl-triflouroacetamide. Samples were analyzed on a Thermo-Finnigan Trace DSQ single-quadrupole MS by electron impact ionization using a 5% phenyl GC column with a 40-300 °C ramp over 16 min.